Christina Heinze-Deml

Artificial Intelligence · 2016–2019

A researcher in the Science corpus: 7 papers; trusts 659 researchers and is trusted by 37 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 178 citations into it; #5,954 of 35,345 by trust here, #9,369 by citations
• Computer Vision and Pattern Recognition — 85 citations into it; #7,856 of 21,575 by trust here, #11,083 by citations
• Statistics and Probability — 59 citations into it; #4,102 of 12,932 by trust here, #3,330 by citations
• Causal Inference & Discovery — 51 citations into it
• Safety, Robustness & Interpretability — 47 citations into it; #707 of 2,251 by trust here, #1,349 by citations
• Representation & Self-Supervised Learning — 40 citations into it
• Molecular Biology — 19 citations into it
• Optimization & Training — 19 citations into it

Whom Christina Heinze-Deml trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Nicolai Meinshausen
2. Diederik P. Kingma
3. Alex Krizhevsky
4. Geoffrey E. Hinton
5. Jan Peters
6. Yann LeCun
7. Kaiming He
8. Yoshua Bengio
9. Bernhard Schölkopf
10. Christian Szegedy

Who trusts Christina Heinze-Deml overall

1. Bernhard Schölkopf
2. Nicolai Meinshausen
3. Fanny Yang
4. Shibani Santurkar
5. Nan Rosemary Ke
6. Jan Peters
7. Divyat Mahajan
8. Rajen Dinesh Shah
9. Jian Lou
10. Yoshua Bengio

Papers from Christina Heinze-Deml's perspective

Ordered by how strongly the researchers Christina Heinze-Deml trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Invariant Causal Prediction for Nonlinear Models (2017) — 315 citations
• Conditional variance penalties and domain shift robustness (2017) — 187 citations
• Causal Structure Learning (2017) — 247 citations
• Invariance-inducing regularization using worst-case transformations suffices to boost accuracy and spatial robustness (2019) — 46 citations
• Guarding Against Adversarial Domain Shifts with Counterfactual Regularization (2017) — 9 citations
• Preserving privacy between features in distributed estimation (2017) — 9 citations
• Predicting the effect of interventions using invariance principles for nonlinear models (2016)